Most techniques to analyze genetic networks monitor changes in the expression of many genes under changing environmental conditions, in different physiological stages, or in different genetic backgrounds to identify genes with similar expression in order to cluster them. However, this approach suffers from the problem that correlated expression can only point to regulatory interactions between genes. It cannot be used to infer such interactions. In addition the number of modules and the number of isolated genes inferred from correlated expression are likely underestimates, because gene perturbation studies preferentially perturb a sample of interesting regulatory genes with many interactions and not an unbiased random sample.